class_docstr: Wide sampling node-centric sampling strategy which gives priority to
  walks containing edges with the highest degree of predicates and
  objects. The degree of a predicate and an object being defined by the
  number of predicates and objects present in its neighborhood, but also by
  their number of occurrence in a Knowledge Graph.
  
  Attributes:
      _is_support_remote: True if the sampling strategy can be used with a
          remote Knowledge Graph, False Otherwise
          Defaults to False.
      _random_state: The random state to use to keep random determinism with
          the sampling strategy.
          Defaults to None.
      _vertices_deg: The degree of the vertices.
          Defaults to {}.
      _visited: Tags vertices that appear at the max depth or of which all
          their children are tagged.
          Defaults to set.
      inverse: True if the inverse algorithm must be used, False otherwise.
          Defaults to False.
      split: True if the split algorithm must be used, False otherwise.
          Defaults to False.
docstr: Fits the sampling strategy by couting the number of available
  neighbors for each vertex, but also by counting the number of
  occurrence that a predicate and an object appears in the Knowledge
  Graph.
  
  Args:
      kg: The Knowledge Graph.
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code: def fit(self, kg: KG) -> None:
      """Fits the sampling strategy by couting the number of available
      neighbors for each vertex, but also by counting the number of
      occurrence that a predicate and an object appears in the Knowledge
      Graph.
  
      Args:
          kg: The Knowledge Graph.
  
      """
      super().fit(kg)
      for vertex in kg._vertices:
          is_reverse = True if vertex.predicate else False
          counter = (
              self._pred_degs if vertex.predicate else self._obj_degs
          )
          self._neighbor_counts[vertex.name] = len(
              kg.get_neighbors(vertex, is_reverse=is_reverse)
          )
  
          if vertex.name in counter:
              counter[vertex.name] += 1
          else:
              counter[vertex.name] = 1
  
file_path: pyRDF2Vec/pyrdf2vec/samplers/wide.py
imports: from collections import defaultdict
  from typing import DefaultDict
  import attr
  from pyrdf2vec.graphs import KG
  from pyrdf2vec.samplers import Sampler
  from pyrdf2vec.typings import Hop
label_desc: Use image node_id 1 for calling the WideSampler obj's underlying member method code with example usage: obj.fit(kg) without return types